Noise-Tolerant Parallel Learning of Geometric Concepts
نویسندگان
چکیده
منابع مشابه
Learning Geometric Concepts with Nasty Noise
We study the efficient learnability of geometric concept classes – specifically, low-degree polynomial threshold functions (PTFs) and intersections of halfspaces – when a fraction of the training data is adversarially corrupted. We give the first polynomial-time PAC learning algorithms for these concept classes with dimension-independent error guarantees in the presence of nasty noise under the...
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ژورنال
عنوان ژورنال: Information and Computation
سال: 1998
ISSN: 0890-5401
DOI: 10.1006/inco.1998.2737